Critical Analysis of the Use of Digital Image Analysis Technology to Monitor Construction Facilities and Materials

Introduction. Due to the latest wide-scale adoption of advanced technologies in the construction sector and the pressure to complete projects in the shortest time and at minimal cost, the principle of achieving a high-quality level in project implementation is a daunting task for those working in construction. In this context digital image analysis technology has served as a viable solution to meet the requirements for improving project management efficiency at different stages. This literature review examines the role of digital image processing technologies in monitoring construction materials and structures throughout the project lifecycle in order to improve their quality and efficiency. The aim of the study is to evaluate the efficiency of this technology compared to traditional methods, review the latest developments in digital image processing for monitoring building structures, and identify performance indicators such as time and cost, as well as mention obstacles preventing its wide-scale adoption in engineering.

Materials and Methods. More than 30 publications (2015-2024) covering AI algorithms (CNN, YOLOv4), 3D modeling (LiDAR, Structure from Motion) and BIM integration were systematically reviewed. Their applicability, scalability, and impact on structural condition monitoring were evaluated.

Research Results. According to the results, the use of digital image analysis technology as a tool for monitoring structures and quality control of construction materials at different stages of a project lifecycle caused improved project quality, reduced time and costs, and boosted decision-making at different stages of a project cycle. The integration of image processing with artificial intelligence and building information modeling systems proved to be accurate in detecting defects in buildings and building materials with a 25% increase in the project management efficiency.

Discussion and Conclusion. Digital image processing (DIP) holds a transformational potential, but it is facing some obstacles such as environmental influences, data heterogeneity and lack of standardization. LiDAR integration, development of sustainable machine learning models for multimodal data analysis, and strengthening interdisciplinary collaboration are set forth. In order to overcome the restraints, further research is required to optimize technologies for real-world operating conditions. DIP is revolutionizing design monitoring, but mass adoption is possible only by means of sustainable innovation, industry-wide partnerships, and adaptation to external factors.

1. Sankarasrinivasan S, Balasubramanian E, Karthik K, Chandrasekar U, Gupta R Health Monitoring of Civil Structures with Integrated UAV and Image Processing System. Procedia Computer Science. 2015;54:508–515. https://doi.org/10.1016/j.procs.2015.06.058

2. Del Savio AA, Luna Torres A, Cárdenas Salas D, Vergara Olivera MA, Urday Ibarra GT Detection and Evaluation of Construction Cracks through Image Analysis Using Computer Vision. Applied Science. 2023;13:16. https://doi.org/10.3390/app13179662

3. Ribeiro D, Santos R, Shibasaki A, Montenegro P, Carvalho H, Calçada R Remote Inspection of RC Structures Using Unmanned Aerial Vehicles and Heuristic Image Processing. Engineering Failure Analysis. 2020;117:104813. https://doi.org/10.1016/j.engfailanal.2020.104813

4. Malesa M, Szczepanek D, Kujawińska M, Świercz A, Kołakowski P Monitoring of Civil Engineering Structures Using Digital Image Correlation Technique. The European Physical Journal Conferences. 2010;6. https://doi.org/10.1051/epjconf/20100631014

5. Galantucci RA, Fatiguso F Advanced Damage Detection Techniques in Historical Buildings Using Digital Photogrammetry and 3D Surface Analysis. Journal of Cultural Heritage. 2019;36:51–62. https://doi.org/10.1016/j.culher.2018.09.014

6. Kim M, Cheng CP, Sohn H, Chang CC A Framework for Dimensional and Surface Quality Assessment of Precast Concrete Elements Using BIM and 3D Laser Scanning. Automation in Construction. 2015;49(B):225–238. http://doi.org/10.1016/j.autcon.2014.07.010

7. Spencer BF Jr, Vedhus H, Narazaki Y Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring. Engineering. 2019;5(2):199–222. https://doi.org/10.1016/j.eng.2018.11.030

8. Tworzewski P, Raczkiewicz W, Czapik P, Tworzewska J Diagnostics of Concrete and Steel in Elements of an Historic Reinforced Concrete Structure. Materials. 2021;14(2):306. https://doi.org/10.3390/ma14020306

9. Nasrollahi M, Bolourian N, Hammad A Concrete Surface Defect Detection Using Deep Neural Network Based on Lidar Scanning. 7th International Construction Conference Jointly With The Construction Research Congress. Canada; 2019. URL: https://www.researchgate.net/publication/335276365_Concrete_Surface_Defect_Detection_Using_Deep_Neural_Network_Based_on_LiDAR_Scanning (accessed: 29.09.2025).

10. McLaughlin E A Deep Learning Approach for Automating Concrete Bridge Defect Assessment Using Computer Vision. Ontario: University of Waterloo; 2020. 112 p. URL: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=qX4dW5sAAAAJ&citation_for_view=qX4dW5sAAAAJ:qjMakFHDy7sC (accessed: 29.09.2025).

11. Talab AMA, Huang Z, Xi Fan, HaiMing L Detection Crack in Image Using Otsu Method and Multiple Filtering in Image Processing Techniques. Optik. 2016;127(3):1030–1033. http://dx.doi.org/10.1016/j.ijleo.2015.09.147

12. Yang J, Park MW, Vela PA, Golparvar-Fard M Construction Performance Monitoring via Still Images, Time-Lapse Photos, and Video Streams: Now, Tomorrow, and the Future. Advanced Engineering Informatics. 2015;29(2):211–224. http://dx.doi.org/10.1016/j.aei.2015.01.011

13. Wijaya U, Yulianto Y, Haryanto E Current Literature Review on Image Processing Analysis for Concrete Damage Assesment. Jurnal Pensil: Pendidikan Teknik Sipil. 2024;13(3):255–274. https://doi.org/10.21009/jpensil.v13i3.45042

14. Meroño JE, Perea AJ, Aguilera MJ, Laguna AM Recognition of Materials and Damage on Historical Buildings Using Digital Image Classification. South African Journal of Science. 2015;111(2):10. https://doi.org/10.17159/sajs.2015/20140001

15. Kim C, Kim B, Kim H 4D CAD Model Updating Using Image Processing-Based Construction Progress Monitoring. Automation in Construction. 2018;35(2):44–52. https://doi.org/10.1016/j.autcon.2013.03.005

16. Moon KH, Falchetto AC, Wistuba MP, Jeong JH Analyzing Aggregate Size Distribution of Asphalt Mixtures Using Simple 2D Digital Image Processing Techniques. Arabian Journal for Science and Engineering. 2015;40:1309–1326. https://doi.org/10.1007/S13369-015-1594-0

17. Alvarez AE, Mora JC, Espinosa LV Quantification of Stone-on-Stone Contact in Permeable Friction Course Mixtures Based on Image Analysis. Construction and Building Materials. 2018;4:462–471. https://doi.org/10.1016/j.conbuildmat.2017.12.189

18. Barbosa FS, Beaucour AL, Farage MCR, Ortola S Image Processing Applied to the Analysis of Segregation in Lightweight Aggregate Concretes. Construction and Building Materials. 2018;25:3375–3381. https://doi.org/10.1016/j.conbuildmat.2011.03.028

19. Breul P, Geoffary JM, Haddani Y On-Site Concrete Segregation Estimation Using Image Analysis. Journal of Advanced Concrete Technology. 2018;6:171–180. https://doi.org/10.3151/jact.6.171